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Displaying 13 results

A widely used model for predicting the 3D structures of proteins from their amino acid sequences.
Container
NVIDIA Parabricks is an accelerated compute framework that supports applications across the genomics industry, primarily supporting analytical workflows for DNA, RNA, and somatic mutation detection applications.
Container
PyG
NVIDIA
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.
Container
Evo 2 is a biological foundation model that is able to integrate information over long genomic sequences.
Container
Evo 2 is a biological foundation model that is able to integrate information over long genomic sequences.
Container
A widely used model for predicting the 3D structures of proteins from their amino acid sequences. This version of the container supports multimers, i.e. proteins made up of 2 or more polypeptide chains.
Container
DeepSap
NVIDIA
DeepSAP is a transformer-based workflow designed to enhance splice junction detection in RNA-seq data.
Container
This container is used for running the data preprocessing part of DeepVariant Training pipeline including make_examples and shuffle
Container
Parabricks Umi_Fgbio is a pipeline that processes sequencing reads with molecular barcodes, and provides impressive error correction and increased accuracy using a sequencing consensus read level.
Container
The Parabricks container based on Amazon Linux 2
Container
MambaSV is a somatic and germline structural variant (SV) caller for long-read sequencing using bidirectional Mamba2 state-space models.
Container
This notebook shows how to retrain DeepVariant models using Parabricks.
Resource
CodonFM predicts masked codons in mRNA sequences from codon-level context to enable variant effect interpretation and codon optimization
Model
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